Data-Science-For-Beginners/2-Working-With-Data/07-python/notebook-covidspread.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimation of COVID-19 Pandemic\n",
    "\n",
    "## Loading Data\n",
    "\n",
    "We will use data on COVID-19 infected individuals, provided by the [Center for Systems Science and Engineering](https://systems.jhu.edu/) (CSSE) at [Johns Hopkins University](https://jhu.edu/). Dataset is available in [this GitHub Repository](https://github.com/CSSEGISandData/COVID-19)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (10,3) # make figures larger"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can load the most recent data directly from GitHub using `pd.read_csv`. If for some reason the data is not available, you can always use the copy available locally in the `data` folder - just uncomment the line below that defines `base_url`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/\" # loading from Internet\n",
    "# base_url = \"../../data/COVID/\" # loading from disk\n",
    "infected_dataset_url = base_url + \"time_series_covid19_confirmed_global.csv\"\n",
    "recovered_dataset_url = base_url + \"time_series_covid19_recovered_global.csv\"\n",
    "deaths_dataset_url = base_url + \"time_series_covid19_deaths_global.csv\"\n",
    "countries_dataset_url = base_url + \"../UID_ISO_FIPS_LookUp_Table.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now load the data for infected individuals and see how the data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>Albania</td>\n",
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       "      <td>Algeria</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>190656</td>\n",
       "      <td>191171</td>\n",
       "      <td>191583</td>\n",
       "      <td>192089</td>\n",
       "      <td>192626</td>\n",
       "      <td>193171</td>\n",
       "      <td>193674</td>\n",
       "      <td>194186</td>\n",
       "      <td>194671</td>\n",
       "      <td>195162</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.50630</td>\n",
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       "      <td>Angola</td>\n",
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       "      <td>46929</td>\n",
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       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
       "\n",
       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  8/20/21  8/21/21  8/22/21  \\\n",
       "0        0        0        0        0  ...   152448   152448   152448   \n",
       "1        0        0        0        0  ...   138132   138790   139324   \n",
       "2        0        0        0        0  ...   190656   191171   191583   \n",
       "3        0        0        0        0  ...    14988    14988    14988   \n",
       "4        0        0        0        0  ...    45583    45817    45945   \n",
       "\n",
       "   8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  8/29/21  \n",
       "0   152583   152660   152722   152822   152960   152960   152960  \n",
       "1   139721   140521   141365   142253   143174   144079   144847  \n",
       "2   192089   192626   193171   193674   194186   194671   195162  \n",
       "3    15002    15003    15014    15016    15025    15025    15025  \n",
       "4    46076    46340    46539    46726    46929    47079    47168  \n",
       "\n",
       "[5 rows x 590 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected = pd.read_csv(infected_dataset_url)\n",
    "infected.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that each row of the table defines the number of infected individuals for each country and/or province, and columns correspond to dates. Similar tables can be loaded for other data, such as number of recovered and number of deaths."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "recovered = pd.read_csv(recovered_dataset_url)\n",
    "deaths = pd.read_csv(deaths_dataset_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making Sense of the Data\n",
    "\n",
    "From the table above the role of province column is not clear. Let's see the different values that are present in `Province/State` column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Australian Capital Territory    1\n",
       "Xinjiang                        1\n",
       "Martinique                      1\n",
       "Guadeloupe                      1\n",
       "French Polynesia                1\n",
       "                               ..\n",
       "Fujian                          1\n",
       "Chongqing                       1\n",
       "Beijing                         1\n",
       "Anhui                           1\n",
       "Turks and Caicos Islands        1\n",
       "Name: Province/State, Length: 87, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected['Province/State'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the names we can deduce that countries like Australia and China have more detailed breakdown by provinces. Let's look for information on China to see the example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
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       "      <th>58</th>\n",
       "      <td>Anhui</td>\n",
       "      <td>China</td>\n",
       "      <td>31.8257</td>\n",
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       "      <th>59</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>40.1824</td>\n",
       "      <td>116.4142</td>\n",
       "      <td>14</td>\n",
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       "      <td>41</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Chongqing</td>\n",
       "      <td>China</td>\n",
       "      <td>30.0572</td>\n",
       "      <td>107.8740</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
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       "      <td>75</td>\n",
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       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Fujian</td>\n",
       "      <td>China</td>\n",
       "      <td>26.0789</td>\n",
       "      <td>117.9874</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
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       "      <td>784</td>\n",
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       "      <td>786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Gansu</td>\n",
       "      <td>China</td>\n",
       "      <td>35.7518</td>\n",
       "      <td>104.2861</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
       "      <td>7</td>\n",
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       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>China</td>\n",
       "      <td>23.3417</td>\n",
       "      <td>113.4244</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>53</td>\n",
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       "      <td>3023</td>\n",
       "      <td>3032</td>\n",
       "      <td>3040</td>\n",
       "      <td>3043</td>\n",
       "      <td>3046</td>\n",
       "      <td>3055</td>\n",
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       "      <th>64</th>\n",
       "      <td>Guangxi</td>\n",
       "      <td>China</td>\n",
       "      <td>23.8298</td>\n",
       "      <td>108.7881</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>36</td>\n",
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       "      <td>...</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
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       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Guizhou</td>\n",
       "      <td>China</td>\n",
       "      <td>26.8154</td>\n",
       "      <td>106.8748</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Hainan</td>\n",
       "      <td>China</td>\n",
       "      <td>19.1959</td>\n",
       "      <td>109.7453</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>19</td>\n",
       "      <td>22</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Hebei</td>\n",
       "      <td>China</td>\n",
       "      <td>39.5490</td>\n",
       "      <td>116.1306</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "      <td>1317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Heilongjiang</td>\n",
       "      <td>China</td>\n",
       "      <td>47.8620</td>\n",
       "      <td>127.7615</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>1614</td>\n",
       "      <td>1614</td>\n",
       "      <td>1614</td>\n",
       "      <td>1614</td>\n",
       "      <td>1614</td>\n",
       "      <td>1614</td>\n",
       "      <td>1615</td>\n",
       "      <td>1615</td>\n",
       "      <td>1615</td>\n",
       "      <td>1615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Henan</td>\n",
       "      <td>China</td>\n",
       "      <td>37.8957</td>\n",
       "      <td>114.9042</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>32</td>\n",
       "      <td>83</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>1521</td>\n",
       "      <td>1522</td>\n",
       "      <td>1523</td>\n",
       "      <td>1524</td>\n",
       "      <td>1525</td>\n",
       "      <td>1525</td>\n",
       "      <td>1527</td>\n",
       "      <td>1528</td>\n",
       "      <td>1528</td>\n",
       "      <td>1528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>China</td>\n",
       "      <td>22.3000</td>\n",
       "      <td>114.2000</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>12049</td>\n",
       "      <td>12052</td>\n",
       "      <td>12057</td>\n",
       "      <td>12062</td>\n",
       "      <td>12069</td>\n",
       "      <td>12074</td>\n",
       "      <td>12077</td>\n",
       "      <td>12094</td>\n",
       "      <td>12100</td>\n",
       "      <td>12107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>China</td>\n",
       "      <td>30.9756</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>444</td>\n",
       "      <td>444</td>\n",
       "      <td>549</td>\n",
       "      <td>761</td>\n",
       "      <td>1058</td>\n",
       "      <td>1423</td>\n",
       "      <td>...</td>\n",
       "      <td>68287</td>\n",
       "      <td>68289</td>\n",
       "      <td>68289</td>\n",
       "      <td>68289</td>\n",
       "      <td>68289</td>\n",
       "      <td>68289</td>\n",
       "      <td>68290</td>\n",
       "      <td>68290</td>\n",
       "      <td>68290</td>\n",
       "      <td>68290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>Hunan</td>\n",
       "      <td>China</td>\n",
       "      <td>27.6104</td>\n",
       "      <td>111.7088</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "      <td>69</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "      <td>1181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>Inner Mongolia</td>\n",
       "      <td>China</td>\n",
       "      <td>44.0935</td>\n",
       "      <td>113.9448</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>China</td>\n",
       "      <td>32.9711</td>\n",
       "      <td>119.4550</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "      <td>33</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>1583</td>\n",
       "      <td>1584</td>\n",
       "      <td>1584</td>\n",
       "      <td>1584</td>\n",
       "      <td>1586</td>\n",
       "      <td>1586</td>\n",
       "      <td>1587</td>\n",
       "      <td>1587</td>\n",
       "      <td>1589</td>\n",
       "      <td>1589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Jiangxi</td>\n",
       "      <td>China</td>\n",
       "      <td>27.6140</td>\n",
       "      <td>115.7221</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "      <td>72</td>\n",
       "      <td>...</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "      <td>937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>Jilin</td>\n",
       "      <td>China</td>\n",
       "      <td>43.6661</td>\n",
       "      <td>126.1923</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "      <td>574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>Liaoning</td>\n",
       "      <td>China</td>\n",
       "      <td>41.2956</td>\n",
       "      <td>122.6085</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>443</td>\n",
       "      <td>443</td>\n",
       "      <td>443</td>\n",
       "      <td>443</td>\n",
       "      <td>443</td>\n",
       "      <td>444</td>\n",
       "      <td>445</td>\n",
       "      <td>446</td>\n",
       "      <td>446</td>\n",
       "      <td>446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Macau</td>\n",
       "      <td>China</td>\n",
       "      <td>22.1667</td>\n",
       "      <td>113.5500</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Ningxia</td>\n",
       "      <td>China</td>\n",
       "      <td>37.2692</td>\n",
       "      <td>106.1655</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Qinghai</td>\n",
       "      <td>China</td>\n",
       "      <td>35.7452</td>\n",
       "      <td>95.9956</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>Shaanxi</td>\n",
       "      <td>China</td>\n",
       "      <td>35.1917</td>\n",
       "      <td>108.8701</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>668</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "      <td>669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Shandong</td>\n",
       "      <td>China</td>\n",
       "      <td>36.3427</td>\n",
       "      <td>118.1498</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>46</td>\n",
       "      <td>75</td>\n",
       "      <td>...</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>923</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Shanghai</td>\n",
       "      <td>China</td>\n",
       "      <td>31.2020</td>\n",
       "      <td>121.4491</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "      <td>40</td>\n",
       "      <td>53</td>\n",
       "      <td>...</td>\n",
       "      <td>2420</td>\n",
       "      <td>2432</td>\n",
       "      <td>2436</td>\n",
       "      <td>2445</td>\n",
       "      <td>2451</td>\n",
       "      <td>2454</td>\n",
       "      <td>2462</td>\n",
       "      <td>2466</td>\n",
       "      <td>2471</td>\n",
       "      <td>2476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Shanxi</td>\n",
       "      <td>China</td>\n",
       "      <td>37.5777</td>\n",
       "      <td>112.2922</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>...</td>\n",
       "      <td>255</td>\n",
       "      <td>255</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>256</td>\n",
       "      <td>258</td>\n",
       "      <td>258</td>\n",
       "      <td>259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>Sichuan</td>\n",
       "      <td>China</td>\n",
       "      <td>30.6171</td>\n",
       "      <td>102.7103</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>28</td>\n",
       "      <td>44</td>\n",
       "      <td>69</td>\n",
       "      <td>...</td>\n",
       "      <td>1181</td>\n",
       "      <td>1182</td>\n",
       "      <td>1183</td>\n",
       "      <td>1185</td>\n",
       "      <td>1185</td>\n",
       "      <td>1185</td>\n",
       "      <td>1186</td>\n",
       "      <td>1187</td>\n",
       "      <td>1188</td>\n",
       "      <td>1188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Tianjin</td>\n",
       "      <td>China</td>\n",
       "      <td>39.3054</td>\n",
       "      <td>117.3230</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>458</td>\n",
       "      <td>459</td>\n",
       "      <td>462</td>\n",
       "      <td>463</td>\n",
       "      <td>464</td>\n",
       "      <td>465</td>\n",
       "      <td>466</td>\n",
       "      <td>466</td>\n",
       "      <td>470</td>\n",
       "      <td>472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>Tibet</td>\n",
       "      <td>China</td>\n",
       "      <td>31.6927</td>\n",
       "      <td>88.0924</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>China</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>Xinjiang</td>\n",
       "      <td>China</td>\n",
       "      <td>41.1129</td>\n",
       "      <td>85.2401</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "      <td>980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Yunnan</td>\n",
       "      <td>China</td>\n",
       "      <td>24.9740</td>\n",
       "      <td>101.4870</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>...</td>\n",
       "      <td>1000</td>\n",
       "      <td>1007</td>\n",
       "      <td>1010</td>\n",
       "      <td>1014</td>\n",
       "      <td>1021</td>\n",
       "      <td>1031</td>\n",
       "      <td>1039</td>\n",
       "      <td>1047</td>\n",
       "      <td>1064</td>\n",
       "      <td>1067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>China</td>\n",
       "      <td>29.1832</td>\n",
       "      <td>120.0934</td>\n",
       "      <td>10</td>\n",
       "      <td>27</td>\n",
       "      <td>43</td>\n",
       "      <td>62</td>\n",
       "      <td>104</td>\n",
       "      <td>128</td>\n",
       "      <td>...</td>\n",
       "      <td>1420</td>\n",
       "      <td>1420</td>\n",
       "      <td>1421</td>\n",
       "      <td>1428</td>\n",
       "      <td>1428</td>\n",
       "      <td>1429</td>\n",
       "      <td>1429</td>\n",
       "      <td>1429</td>\n",
       "      <td>1429</td>\n",
       "      <td>1430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>34 rows × 590 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State Country/Region      Lat      Long  1/22/20  1/23/20  \\\n",
       "58           Anhui          China  31.8257  117.2264        1        9   \n",
       "59         Beijing          China  40.1824  116.4142       14       22   \n",
       "60       Chongqing          China  30.0572  107.8740        6        9   \n",
       "61          Fujian          China  26.0789  117.9874        1        5   \n",
       "62           Gansu          China  35.7518  104.2861        0        2   \n",
       "63       Guangdong          China  23.3417  113.4244       26       32   \n",
       "64         Guangxi          China  23.8298  108.7881        2        5   \n",
       "65         Guizhou          China  26.8154  106.8748        1        3   \n",
       "66          Hainan          China  19.1959  109.7453        4        5   \n",
       "67           Hebei          China  39.5490  116.1306        1        1   \n",
       "68    Heilongjiang          China  47.8620  127.7615        0        2   \n",
       "69           Henan          China  37.8957  114.9042        5        5   \n",
       "70       Hong Kong          China  22.3000  114.2000        0        2   \n",
       "71           Hubei          China  30.9756  112.2707      444      444   \n",
       "72           Hunan          China  27.6104  111.7088        4        9   \n",
       "73  Inner Mongolia          China  44.0935  113.9448        0        0   \n",
       "74         Jiangsu          China  32.9711  119.4550        1        5   \n",
       "75         Jiangxi          China  27.6140  115.7221        2        7   \n",
       "76           Jilin          China  43.6661  126.1923        0        1   \n",
       "77        Liaoning          China  41.2956  122.6085        2        3   \n",
       "78           Macau          China  22.1667  113.5500        1        2   \n",
       "79         Ningxia          China  37.2692  106.1655        1        1   \n",
       "80         Qinghai          China  35.7452   95.9956        0        0   \n",
       "81         Shaanxi          China  35.1917  108.8701        0        3   \n",
       "82        Shandong          China  36.3427  118.1498        2        6   \n",
       "83        Shanghai          China  31.2020  121.4491        9       16   \n",
       "84          Shanxi          China  37.5777  112.2922        1        1   \n",
       "85         Sichuan          China  30.6171  102.7103        5        8   \n",
       "86         Tianjin          China  39.3054  117.3230        4        4   \n",
       "87           Tibet          China  31.6927   88.0924        0        0   \n",
       "88         Unknown          China      NaN       NaN        0        0   \n",
       "89        Xinjiang          China  41.1129   85.2401        0        2   \n",
       "90          Yunnan          China  24.9740  101.4870        1        2   \n",
       "91        Zhejiang          China  29.1832  120.0934       10       27   \n",
       "\n",
       "    1/24/20  1/25/20  1/26/20  1/27/20  ...  8/20/21  8/21/21  8/22/21  \\\n",
       "58       15       39       60       70  ...     1008     1008     1008   \n",
       "59       36       41       68       80  ...     1112     1113     1115   \n",
       "60       27       57       75      110  ...      603      603      603   \n",
       "61       10       18       35       59  ...      780      780      780   \n",
       "62        2        4        7       14  ...      199      199      199   \n",
       "63       53       78      111      151  ...     3001     3007     3012   \n",
       "64       23       23       36       46  ...      289      289      289   \n",
       "65        3        4        5        7  ...      147      147      147   \n",
       "66        8       19       22       33  ...      190      190      190   \n",
       "67        2        8       13       18  ...     1317     1317     1317   \n",
       "68        4        9       15       21  ...     1614     1614     1614   \n",
       "69        9       32       83      128  ...     1521     1522     1523   \n",
       "70        2        5        8        8  ...    12049    12052    12057   \n",
       "71      549      761     1058     1423  ...    68287    68289    68289   \n",
       "72       24       43       69      100  ...     1181     1181     1181   \n",
       "73        1        7        7       11  ...      412      412      412   \n",
       "74        9       18       33       47  ...     1583     1584     1584   \n",
       "75       18       18       36       72  ...      937      937      937   \n",
       "76        3        4        4        6  ...      574      574      574   \n",
       "77        4       17       21       27  ...      443      443      443   \n",
       "78        2        2        5        6  ...       63       63       63   \n",
       "79        2        3        4        7  ...       77       77       77   \n",
       "80        0        1        1        6  ...       18       18       18   \n",
       "81        5       15       22       35  ...      668      668      668   \n",
       "82       15       27       46       75  ...      923      923      923   \n",
       "83       20       33       40       53  ...     2420     2432     2436   \n",
       "84        1        6        9       13  ...      255      255      256   \n",
       "85       15       28       44       69  ...     1181     1182     1183   \n",
       "86        8       10       14       23  ...      458      459      462   \n",
       "87        0        0        0        0  ...        1        1        1   \n",
       "88        0        0        0        0  ...        0        0        0   \n",
       "89        2        3        4        5  ...      980      980      980   \n",
       "90        5       11       16       26  ...     1000     1007     1010   \n",
       "91       43       62      104      128  ...     1420     1420     1421   \n",
       "\n",
       "    8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  8/29/21  \n",
       "58     1008     1008     1008     1008     1008     1008     1008  \n",
       "59     1115     1115     1115     1115     1115     1115     1115  \n",
       "60      603      603      603      603      603      603      603  \n",
       "61      782      782      783      783      784      785      786  \n",
       "62      199      199      199      199      199      199      199  \n",
       "63     3020     3023     3032     3040     3043     3046     3055  \n",
       "64      289      289      289      289      289      289      289  \n",
       "65      147      147      147      147      147      147      147  \n",
       "66      190      190      190      190      190      190      190  \n",
       "67     1317     1317     1317     1317     1317     1317     1317  \n",
       "68     1614     1614     1614     1615     1615     1615     1615  \n",
       "69     1524     1525     1525     1527     1528     1528     1528  \n",
       "70    12062    12069    12074    12077    12094    12100    12107  \n",
       "71    68289    68289    68289    68290    68290    68290    68290  \n",
       "72     1181     1181     1181     1181     1181     1181     1181  \n",
       "73      412      412      412      412      412      412      412  \n",
       "74     1584     1586     1586     1587     1587     1589     1589  \n",
       "75      937      937      937      937      937      937      937  \n",
       "76      574      574      574      574      574      574      574  \n",
       "77      443      443      444      445      446      446      446  \n",
       "78       63       63       63       63       63       63       63  \n",
       "79       77       77       77       77       77       77       77  \n",
       "80       18       18       18       18       18       18       18  \n",
       "81      669      669      669      669      669      669      669  \n",
       "82      923      923      923      923      923      923      924  \n",
       "83     2445     2451     2454     2462     2466     2471     2476  \n",
       "84      256      256      256      256      258      258      259  \n",
       "85     1185     1185     1185     1186     1187     1188     1188  \n",
       "86      463      464      465      466      466      470      472  \n",
       "87        1        1        1        1        1        1        1  \n",
       "88        0        0        0        0        0        0        0  \n",
       "89      980      980      980      980      980      980      980  \n",
       "90     1014     1021     1031     1039     1047     1064     1067  \n",
       "91     1428     1428     1429     1429     1429     1429     1430  \n",
       "\n",
       "[34 rows x 590 columns]"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected[infected['Country/Region']=='China']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-processing the Data \n",
    "\n",
    "We are not interested in breaking countries down to further territories, thus we would first get rid of this breakdown and add information on all territories together, to get info for the whole country. This can be done using `groupby`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>1/28/20</th>\n",
       "      <th>1/29/20</th>\n",
       "      <th>...</th>\n",
       "      <th>8/20/21</th>\n",
       "      <th>8/21/21</th>\n",
       "      <th>8/22/21</th>\n",
       "      <th>8/23/21</th>\n",
       "      <th>8/24/21</th>\n",
       "      <th>8/25/21</th>\n",
       "      <th>8/26/21</th>\n",
       "      <th>8/27/21</th>\n",
       "      <th>8/28/21</th>\n",
       "      <th>8/29/21</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country/Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>33.93911</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>152448</td>\n",
       "      <td>152448</td>\n",
       "      <td>152448</td>\n",
       "      <td>152583</td>\n",
       "      <td>152660</td>\n",
       "      <td>152722</td>\n",
       "      <td>152822</td>\n",
       "      <td>152960</td>\n",
       "      <td>152960</td>\n",
       "      <td>152960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>41.15330</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>138132</td>\n",
       "      <td>138790</td>\n",
       "      <td>139324</td>\n",
       "      <td>139721</td>\n",
       "      <td>140521</td>\n",
       "      <td>141365</td>\n",
       "      <td>142253</td>\n",
       "      <td>143174</td>\n",
       "      <td>144079</td>\n",
       "      <td>144847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>28.03390</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>190656</td>\n",
       "      <td>191171</td>\n",
       "      <td>191583</td>\n",
       "      <td>192089</td>\n",
       "      <td>192626</td>\n",
       "      <td>193171</td>\n",
       "      <td>193674</td>\n",
       "      <td>194186</td>\n",
       "      <td>194671</td>\n",
       "      <td>195162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Andorra</th>\n",
       "      <td>42.50630</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>14988</td>\n",
       "      <td>14988</td>\n",
       "      <td>14988</td>\n",
       "      <td>15002</td>\n",
       "      <td>15003</td>\n",
       "      <td>15014</td>\n",
       "      <td>15016</td>\n",
       "      <td>15025</td>\n",
       "      <td>15025</td>\n",
       "      <td>15025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>-11.20270</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>45583</td>\n",
       "      <td>45817</td>\n",
       "      <td>45945</td>\n",
       "      <td>46076</td>\n",
       "      <td>46340</td>\n",
       "      <td>46539</td>\n",
       "      <td>46726</td>\n",
       "      <td>46929</td>\n",
       "      <td>47079</td>\n",
       "      <td>47168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 588 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Lat       Long  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
       "Country/Region                                                            \n",
       "Afghanistan     33.93911  67.709953        0        0        0        0   \n",
       "Albania         41.15330  20.168300        0        0        0        0   \n",
       "Algeria         28.03390   1.659600        0        0        0        0   \n",
       "Andorra         42.50630   1.521800        0        0        0        0   \n",
       "Angola         -11.20270  17.873900        0        0        0        0   \n",
       "\n",
       "                1/26/20  1/27/20  1/28/20  1/29/20  ...  8/20/21  8/21/21  \\\n",
       "Country/Region                                      ...                     \n",
       "Afghanistan           0        0        0        0  ...   152448   152448   \n",
       "Albania               0        0        0        0  ...   138132   138790   \n",
       "Algeria               0        0        0        0  ...   190656   191171   \n",
       "Andorra               0        0        0        0  ...    14988    14988   \n",
       "Angola                0        0        0        0  ...    45583    45817   \n",
       "\n",
       "                8/22/21  8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  \\\n",
       "Country/Region                                                                  \n",
       "Afghanistan      152448   152583   152660   152722   152822   152960   152960   \n",
       "Albania          139324   139721   140521   141365   142253   143174   144079   \n",
       "Algeria          191583   192089   192626   193171   193674   194186   194671   \n",
       "Andorra           14988    15002    15003    15014    15016    15025    15025   \n",
       "Angola            45945    46076    46340    46539    46726    46929    47079   \n",
       "\n",
       "                8/29/21  \n",
       "Country/Region           \n",
       "Afghanistan      152960  \n",
       "Albania          144847  \n",
       "Algeria          195162  \n",
       "Andorra           15025  \n",
       "Angola            47168  \n",
       "\n",
       "[5 rows x 588 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infected = infected.groupby('Country/Region').sum()\n",
    "recovered = recovered.groupby('Country/Region').sum()\n",
    "deaths = deaths.groupby('Country/Region').sum()\n",
    "\n",
    "infected.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that due to using `groupby` all DataFrames are now indexed by Country/Region. We can thus access the data for a specific country by using `.loc`:|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "infected.loc['US'][2:].plot()\n",
    "recovered.loc['US'][2:].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Note** how we use `[2:]` to remove first two elements of a sequence that contain geolocation of a country. We can also drop those two columns altogether:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "infected.drop(columns=['Lat','Long'],inplace=True)\n",
    "recovered.drop(columns=['Lat','Long'],inplace=True)\n",
    "deaths.drop(columns=['Lat','Long'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Investigating the Data\n",
    "\n",
    "Let's now switch to investigating a specific country. Let's create a frame that contains the data on infections indexed by date:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>infected</th>\n",
       "      <th>recovered</th>\n",
       "      <th>deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-22</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-25</th>\n",
       "      <td>38223029</td>\n",
       "      <td>0</td>\n",
       "      <td>632272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-26</th>\n",
       "      <td>38384360</td>\n",
       "      <td>0</td>\n",
       "      <td>633564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-27</th>\n",
       "      <td>38707294</td>\n",
       "      <td>0</td>\n",
       "      <td>636720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-28</th>\n",
       "      <td>38760363</td>\n",
       "      <td>0</td>\n",
       "      <td>637254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-29</th>\n",
       "      <td>38796746</td>\n",
       "      <td>0</td>\n",
       "      <td>637531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>586 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            infected  recovered  deaths\n",
       "2020-01-22         1          0       0\n",
       "2020-01-23         1          0       0\n",
       "2020-01-24         2          0       0\n",
       "2020-01-25         2          0       0\n",
       "2020-01-26         5          0       0\n",
       "...              ...        ...     ...\n",
       "2021-08-25  38223029          0  632272\n",
       "2021-08-26  38384360          0  633564\n",
       "2021-08-27  38707294          0  636720\n",
       "2021-08-28  38760363          0  637254\n",
       "2021-08-29  38796746          0  637531\n",
       "\n",
       "[586 rows x 3 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mkframe(country):\n",
    "    df = pd.DataFrame({ 'infected' : infected.loc[country] ,\n",
    "                        'recovered' : recovered.loc[country],\n",
    "                        'deaths' : deaths.loc[country]})\n",
    "    df.index = pd.to_datetime(df.index)\n",
    "    return df\n",
    "\n",
    "df = mkframe('US')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's compute the number of new infected people each day. This will allow us to see the speed at which pandemic progresses. The easiest day to do it is to use `diff`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ninfected'] = df['infected'].diff()\n",
    "df['ninfected'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see high fluctuations in data. Let's look closer at one of the months:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[(df.index.year==2020) & (df.index.month==7)]['ninfected'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It clearly looks like there are weekly fluctuations in data. Because we want to be able to see the trends, it makes sense to smooth out the curve by computing running average (i.e. for each day we will compute the average value of the previous several days):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ninfav'] = df['ninfected'].rolling(window=7).mean()\n",
    "df['ninfav'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to be able to compare several countries, we might want to take the country's population into account, and compare the percentage of infected individuals with respect to country's population. In order to get country's population, let's load the dataset of countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UID</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>code3</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>Combined_Key</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>AF</td>\n",
       "      <td>AFG</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.939110</td>\n",
       "      <td>67.709953</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38928341.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>AL</td>\n",
       "      <td>ALB</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2877800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>DZ</td>\n",
       "      <td>DZA</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>43851043.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>AD</td>\n",
       "      <td>AND</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>77265.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>AO</td>\n",
       "      <td>AGO</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>Angola</td>\n",
       "      <td>32866268.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>4191</th>\n",
       "      <td>84056037</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56037.0</td>\n",
       "      <td>Sweetwater</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>41.659439</td>\n",
       "      <td>-108.882788</td>\n",
       "      <td>Sweetwater, Wyoming, US</td>\n",
       "      <td>42343.0</td>\n",
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       "    <tr>\n",
       "      <th>4192</th>\n",
       "      <td>84056039</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56039.0</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>43.935225</td>\n",
       "      <td>-110.589080</td>\n",
       "      <td>Teton, Wyoming, US</td>\n",
       "      <td>23464.0</td>\n",
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       "    <tr>\n",
       "      <th>4193</th>\n",
       "      <td>84056041</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56041.0</td>\n",
       "      <td>Uinta</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>41.287818</td>\n",
       "      <td>-110.547578</td>\n",
       "      <td>Uinta, Wyoming, US</td>\n",
       "      <td>20226.0</td>\n",
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       "    <tr>\n",
       "      <th>4194</th>\n",
       "      <td>84056043</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56043.0</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>43.904516</td>\n",
       "      <td>-107.680187</td>\n",
       "      <td>Washakie, Wyoming, US</td>\n",
       "      <td>7805.0</td>\n",
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       "    <tr>\n",
       "      <th>4195</th>\n",
       "      <td>84056045</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>56045.0</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>US</td>\n",
       "      <td>43.839612</td>\n",
       "      <td>-104.567488</td>\n",
       "      <td>Weston, Wyoming, US</td>\n",
       "      <td>6927.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4196 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           UID iso2 iso3  code3     FIPS      Admin2 Province_State  \\\n",
       "0            4   AF  AFG    4.0      NaN         NaN            NaN   \n",
       "1            8   AL  ALB    8.0      NaN         NaN            NaN   \n",
       "2           12   DZ  DZA   12.0      NaN         NaN            NaN   \n",
       "3           20   AD  AND   20.0      NaN         NaN            NaN   \n",
       "4           24   AO  AGO   24.0      NaN         NaN            NaN   \n",
       "...        ...  ...  ...    ...      ...         ...            ...   \n",
       "4191  84056037   US  USA  840.0  56037.0  Sweetwater        Wyoming   \n",
       "4192  84056039   US  USA  840.0  56039.0       Teton        Wyoming   \n",
       "4193  84056041   US  USA  840.0  56041.0       Uinta        Wyoming   \n",
       "4194  84056043   US  USA  840.0  56043.0    Washakie        Wyoming   \n",
       "4195  84056045   US  USA  840.0  56045.0      Weston        Wyoming   \n",
       "\n",
       "     Country_Region        Lat       Long_             Combined_Key  \\\n",
       "0       Afghanistan  33.939110   67.709953              Afghanistan   \n",
       "1           Albania  41.153300   20.168300                  Albania   \n",
       "2           Algeria  28.033900    1.659600                  Algeria   \n",
       "3           Andorra  42.506300    1.521800                  Andorra   \n",
       "4            Angola -11.202700   17.873900                   Angola   \n",
       "...             ...        ...         ...                      ...   \n",
       "4191             US  41.659439 -108.882788  Sweetwater, Wyoming, US   \n",
       "4192             US  43.935225 -110.589080       Teton, Wyoming, US   \n",
       "4193             US  41.287818 -110.547578       Uinta, Wyoming, US   \n",
       "4194             US  43.904516 -107.680187    Washakie, Wyoming, US   \n",
       "4195             US  43.839612 -104.567488      Weston, Wyoming, US   \n",
       "\n",
       "      Population  \n",
       "0     38928341.0  \n",
       "1      2877800.0  \n",
       "2     43851043.0  \n",
       "3        77265.0  \n",
       "4     32866268.0  \n",
       "...          ...  \n",
       "4191     42343.0  \n",
       "4192     23464.0  \n",
       "4193     20226.0  \n",
       "4194      7805.0  \n",
       "4195      6927.0  \n",
       "\n",
       "[4196 rows x 12 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries = pd.read_csv(countries_dataset_url)\n",
    "countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because this dataset contains information on both countries and provinces, to get the population of the whole country we need to be a little bit clever: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UID</th>\n",
       "      <th>iso2</th>\n",
       "      <th>iso3</th>\n",
       "      <th>code3</th>\n",
       "      <th>FIPS</th>\n",
       "      <th>Admin2</th>\n",
       "      <th>Province_State</th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long_</th>\n",
       "      <th>Combined_Key</th>\n",
       "      <th>Population</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>840</td>\n",
       "      <td>US</td>\n",
       "      <td>USA</td>\n",
       "      <td>840.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>40.0</td>\n",
       "      <td>-100.0</td>\n",
       "      <td>US</td>\n",
       "      <td>329466283.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "     UID iso2 iso3  code3  FIPS Admin2 Province_State Country_Region   Lat  \\\n",
       "790  840   US  USA  840.0   NaN    NaN            NaN             US  40.0   \n",
       "\n",
       "     Long_ Combined_Key   Population  \n",
       "790 -100.0           US  329466283.0  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "countries[(countries['Country_Region']=='US') & countries['Province_State'].isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "pop = countries[(countries['Country_Region']=='US') & countries['Province_State'].isna()]['Population'].iloc[0]\n",
    "df['pinfected'] = df['infected']*100 / pop\n",
    "df['pinfected'].plot(figsize=(10,3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Computing $R_t$\n",
    "\n",
    "To see how infectious is the disease, we look at the **basic reproduction number** $R_0$, which indicated the number of people that an infected person would further infect. When $R_0$ is more than 1, the epidemic is likely to spread.\n",
    "\n",
    "$R_0$ is a property of the disease itself, and does not take into account some protective measures that people may take to slow down the pandemic. During the pandemic progression, we can estimate the reproduction number $R_t$ at any given time $t$. It has been shown that this number can be roughly estimated by taking a window of 8 days, and computing $$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$\n",
    "where $I_t$ is the number of newly infected individuals on day $t$.\n",
    "\n",
    "Let's compute $R_t$ for our pandemic data. To do this, we will take a rolling window of 8 `ninfected` values, and apply the function to compute the ratio above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Rt'] = df['ninfected'].rolling(8).apply(lambda x: x[4:].sum()/x[:4].sum())\n",
    "df['Rt'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that there are some gaps in the graph. Those can be caused by either `NaN`, if  `inf` values being present in the dataset. `inf` may be caused by division by 0, and `NaN` can indicate missing data, or no data available to compute the result (like in the very beginning of our frame, where rolling window of width 8 is not yet available). To make the graph nicer, we need to fill those values using `replace` and `fillna` function.\n",
    "\n",
    "Let's further look at the beginning of the pandemic. We will also limit the y-axis values to show only values below 6, in order to see better, and draw horizontal line at 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[df.index<\"2020-05-01\"]['Rt'].replace(np.inf,np.nan).fillna(method='pad').plot(figsize=(10,3))\n",
    "ax.set_ylim([0,6])\n",
    "ax.axhline(1,linestyle='--',color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another interesting indicator of the pandemic is the **derivative**, or **daily difference** in new cases. It allows us to see clearly when pandemic is increasing or declining. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['ninfected'].diff().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the fact that there are a lot of fluctuations in data caused by reporting, it makes sense to smooth the curve by running rolling average to get the overall picture. Let's again focus on the first months of the pandemic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
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    "ax=df[df.index<\"2020-06-01\"]['ninfected'].diff().rolling(7).mean().plot()\n",
    "ax.axhline(0,linestyle='-.',color='red')\n",
    "plt.show()"
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    "\n",
    "## Challenge\n",
    "\n",
    "Now it is time for you to play more with the code and data! Here are a few suggestions you can experiment with:\n",
    "* See the spread of the pandemic in different countries.\n",
    "* Plot $R_t$ graphs for several countries on one plot for comparison, or make several plots side-by-side\n",
    "* See how the number of deaths and recoveries correlate with number of infected cases.\n",
    "* Try to find out how long a typical disease lasts by visually correlating infection rate and deaths rate and looking for some anomalies. You may need to look at different countries to find that out.\n",
    "* Calculate the fatality rate and how it changes over time. You may want to take into account the length of the disease in days to shift one time series before doing calculations"
   ]
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    "## References\n",
    "\n",
    "You may look at further studies of COVID epidemic spread in the following publications:\n",
    "* [Sliding SIR Model for Rt Estimation during COVID Pandemic](https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/), blog post by [Dmitry Soshnikov](http://soshnikov.com)\n",
    "* T.Petrova, D.Soshnikov, A.Grunin. [Estimation of Time-Dependent Reproduction Number for Global COVID-19 Outbreak](https://www.preprints.org/manuscript/202006.0289/v1). *Preprints* **2020**, 2020060289 (doi: 10.20944/preprints202006.0289.v1)\n",
    "* [Code for the above paper on GitHub](https://github.com/shwars/SlidingSIR)"
   ]
  },
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